• Humans
• Electronic Health Records
• Algorithms

• AI
• Emergency Department
• design
• literature review
• sensors

Gastrointestinal (GI) complications frequently necessitate intensive care unit (ICU) admission. Additionally, critically ill patients also develop GI complications requiring further diagnostic and therapeutic interventions. However, these patients form a vulnerable group, who are at risk for developing side effects and complications. Every effort must be made to reduce invasiveness and ensure safety of interventions in ICU patients. Artificial intelligence (AI) is a rapidly evolving technology with several potential applications in healthcare settings. ICUs produce a large amount of data, which may be employed for creation of AI algorithms, and provide a lucrative opportunity for application of AI. However, the current role of AI in these patients remains limited due to lack of large-scale trials comparing the efficacy of AI with the accepted standards of care.

• Artificial intelligence
• Critical care
• Gastroenterology
• Hepatology
• Intensive care unit
• Machine learning

Upper gastrointestinal bleeding is a common gastrointes-tinal condition with a high incidence rate and great harm to the body. This paper reviews the rebleeding, endos-copic intervention, and death risk prediction models for upper gastrointestinal bleeding, including the study population, research methods, related risk factors, and model performance, in order to provide reference for clinicians to conduct risk assessment as soon as possible, formulate effective prevention and management plans, and improve patient survival outcomes.

• Upper gastrointestinal bleeding
• Rebleeding
• Endoscopic intervention
• Death risk
• Prediction models

• contrast enhancement
• data augmentation
• deep learning
• gastrointestinal disease
• ulcer disease classification

• Humans
• Gastrointestinal Hemorrhage/diagnosis
• Gastrointestinal Hemorrhage/etiology
• Gastrointestinal Hemorrhage/therapy
• Risk Factors
• Acute Disease
• Emergency Service, Hospital
• Tomography

• Anticoagulants
• Colonic Polyps/pathology
• Colonoscopy/methods
• Gastrointestinal Hemorrhage/chemically induced
• Gastrointestinal Hemorrhage/prevention & control
• Humans
• Postoperative Hemorrhage/epidemiology
• Postoperative Hemorrhage/etiology
• Postoperative Hemorrhage/prevention & control
• Propensity Score
• Retrospective Studies

Bleeding is one of the major complications after endoscopic submucosal dissection (ESD) in early gastric cancer (EGC) patients. There are limited studies on estimating the bleeding risk after ESD using an artificial intelligence system.

To derivate and verify the performance of the deep learning model and the clinical model for predicting bleeding risk after ESD in EGC patients.

Patients with EGC who underwent ESD between January 2010 and June 2020 at the Samsung Medical Center were enrolled, and post-ESD bleeding (PEB) was investigated retrospectively. We split the entire cohort into a development set (80%) and a validation set (20%). The deep learning and clinical model were built on the development set and tested in the validation set. The performance of the deep learning model and the clinical model were compared using the area under the curve and the stratification of bleeding risk after ESD.

A total of 5629 patients were included, and PEB occurred in 325 patients. The area under the curve for predicting PEB was 0.71 (95% confidence interval: 0.63-0.78) in the deep learning model and 0.70 (95% confidence interval: 0.62-0.77) in the clinical model, without significant difference (P = 0.730). The patients expected to the low- (< 5%), intermediate- (≥ 5%, < 9%), and high-risk (≥ 9%) categories were observed with actual bleeding rate of 2.2%, 3.9%, and 11.6%, respectively, in the deep learning model; 4.0%, 8.8%, and 18.2%, respectively, in the clinical model.

A deep learning model can predict and stratify the bleeding risk after ESD in patients with EGC.

• Clinical model
• Deep learning model
• Post-endoscopic submucosal dissection bleeding
• Stratification of bleeding risk

• Artificial Intelligence
• Deep Learning
• Endoscopic Mucosal Resection/adverse effects
• Gastric Mucosa/surgery
• Humans
• Postoperative Hemorrhage/epidemiology
• Postoperative Hemorrhage/etiology
• Retrospective Studies
• Stomach Neoplasms/complications

• Big data, Future of healthcare, Machine learning, Neural networks

• ALT, alanine aminotransferase
• APRI, aspartate transaminase-to-platelet ratio index
• AUROC, area under the receiver operating characteristic curve
• Antiviral treatment
• CDARS, Clinical Data Analysis and Reporting System
• CHB, chronic hepatitis B
• CHC, chronic hepatitis C
• CI, confidence intervals
• CVH, chronic viral hepatitis
• Cirrhosis
• DM, diabetes mellitus
• HADCL, Hospital Authority Data Collaboration Lab
• HBV, hepatitis B virus
• HBeAg, hepatitis B e antigen
• HBsAg, hepatitis B surface antigen
• HCC, hepatocellular carcinoma
• ICD-9-CM, International Classification of Diseases, Ninth Revision Clinical Modification
• Liver cancer
• Mortality
• NA, nucleos(t)ide analogue
• RS, ridge score
• WHO, World Health Organization
• World Health Organization
• aHR, adjusted hazard ratio
• anti-HCV, antibody to hepatitis C virus

• Emergency endoscopic hemostasis
• LASSO
• Nomogram
• Peptic ulcer bleeding
• Rebleeding

• Hemostasis, Endoscopic
• Humans
• Peptic Ulcer
• Peptic Ulcer Hemorrhage/therapy
• Recurrence
• Retrospective Studies

• artificial intelligence
• complications
• convolutional neural network
• gastric ulcer
• peptic ulcer

• National Natural Science Foundation of China

• cardiovascular complications
• coagulation
• colonoscopy
• endoscopic polypectomy
• gastrointestinal bleeding

• Aged
• Anticoagulants/adverse effects
• Blood Transfusion/statistics & numerical data
• Cohort Studies
• Colonic Polyps/surgery
• Colonoscopy
• Dabigatran/adverse effects
• Factor Xa Inhibitors/adverse effects
• Gastrointestinal Hemorrhage/epidemiology
• Hong Kong/epidemiology
• Humans
• Male
• Postoperative Complications/epidemiology
• Pyrazoles/adverse effects
• Pyridones/adverse effects
• Retrospective Studies
• Risk Assessment
• Rivaroxaban/adverse effects
• Thromboembolism/epidemiology
• Warfarin/adverse effects

The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.

• Artificial intelligence
• Machine learning
• Deep learning
• Clinical applications
• Gastroenterology

• Artificial Intelligence
• Barrett Esophagus
• Diagnostic Imaging
• Endoscopy
• Gastroenterology
• Humans

• Acute appendicitis
• Acute cholecystitis
• Acute pancreatitis
• Artificial neural networks
• Bowel obstruction
• Decision support system
• Emergency surgery
• Peptic ulcer bleeding
• Perforated gastroduodenal ulcers
• Strangulated hernias

• Acute Disease
• Artificial Intelligence
• Cholecystitis/surgery
• Humans
• Neural Networks, Computer
• Pancreatitis

• Università degli Studi di Brescia

• artificial intelligence
• bleeding
• deep learning
• peptic ulcer

• Artificial intelligence
• Deep learning
• Machine learning
• Prognosis prediction
• Small intestinal diseases

• Artificial Intelligence
• Capsule Endoscopy
• Gastrointestinal Diseases/diagnosis
• Humans
• Intestinal Diseases/diagnosis
• Intestine, Small

• Barrett’s esophagus
• Taiwan
• computer
• logistic models
• neural networks

• Adult
• Asia
• Barrett Esophagus/diagnosis
• Barrett Esophagus/epidemiology
• Esophageal Neoplasms/diagnosis
• Esophageal Neoplasms/epidemiology
• Humans
• Retrospective Studies
• Taiwan/epidemiology

Artificial intelligence (AI) has been widely involved in every aspect of healthcare in the preclinical stage. In the digestive system, AI has been trained to assist auxiliary examinations including histopathology, endoscopy, ultrasonography, computerized tomography, and magnetic resonance imaging in detection, diagnosis, classification, differentiation, prognosis, and quality control. In the field of endoscopy, the application of AI, such as automatic detection, diagnosis, classification, and invasion depth, in early gastrointestinal (GI) cancers has received wide attention. There is a paucity of studies of AI application on common GI benign diseases based on endoscopy. In the review, we provide an overview of AI applications to endoscopy on common GI benign diseases including in the esophagus, stomach, intestine, and colon. It indicates that AI will gradually become an indispensable part of normal endoscopic detection and diagnosis of common GI benign diseases as clinical data, algorithms, and other related work are constantly repeated and improved.

• Artificial intelligence
• Endoscopy
• Common gastrointestinal benign diseases

• Adenocarcinoma
• Artificial intelligence
• Barrett
• Convolutional neural networks
• Deep learning

• artificial intelligence
• endoscopy
• lower gastrointestinal tract
• quality
• upper gastrointestinal tract

• Artificial intelligence
• Computer-aided detection
• Computer-aided diagnosis
• Quality
• Upper and lower gastrointestinal tract

• Artificial intelligence
• capsule endoscopy
• colon polyp
• colonoscopy
• esophageal squamous cell carcinoma
• esophagogastroduodenoscopy
• gastric cancer
• helicobacter pylori
• magnified endoscopy
• narrow band imaging

Gastrointestinal (GI) endoscopy is the central element in contemporary gastroenterology as it provides direct evidence to guide targeted therapy. To increase the accuracy of GI endoscopy and to reduce human-related errors, artificial intelligence (AI) has been applied in GI endoscopy, which has been proved to be effective in diagnosing and treating numerous diseases. Therefore, we review current research on the efficacy of AI-assisted GI endoscopy in order to assess its functions, advantages and how the design can be improved.

• Gastrointestinal endoscopy
• Artificial intelligence
• Diagnosis
• Advantages

• endoscopy
• gastrointestinal bleeding
• guidelines
• hemostasis
• non-steroidal anti-inflammatory drugs
• risk factors

• Deep Learning
• Digestive System
• Machine Learning
• Neural Network